Skip to main content
Log in

Constructing 3D facial hierarchical structure based on surface measurements

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a novel framework for 3D facial similarity measures and facial data organization. The 3D facial similarity measures of our method are based on iso-geodesic stripes and conformal parameterization. Using the conformal parameterization, the 3D facial surface can be mapped into a 2D domain and the iso-geodesic stripes of the face can be measured. The measure results can be regarded as the similarity of faces, which is robust to head poses and facial expressions. Based on the measure result, a hierarchical structure of faces can be constructed, which is used to organize different faces. The structure can be utilized to accelerate the face searching speed in a large database. In experiment, we construct the hierarchical structures from two public facial databases: Gavab and Texas3D. The searching speed based on the structure can be increased by 4-6 times without accuracy loss of recognition.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Ahdid R, Safi S, Manaut B (2015) Three Dimensional Face Surfaces Analysis using Geodesic Distance. J Comput Sci Appl [J] 3:67–72

    Google Scholar 

  2. Antipov G, Baccouche M, Berrani SA, Dugelay JL (2016) Apparent age estimation from face images combining general and Children-Specialized deep learning models. IEEE Conference on Computer Vision and Pattern Recognition Workshops [C] 1:801–809

    Google Scholar 

  3. Ballihi L, Amor BB, Daoudi M, Srivastava A (2012) Geometric Based 3D Facial Gender Classifi-CatioN. In: International Symposium on Communications Control and Signal Processing [J], pp 1–5. https://doi.org/10.1109/ISCCSP.2012.6217828

  4. Berretti S, Bimbo D, Vicario E (2003) Weighted walkthroughs between extended entities for retrieval by spatial arrangement. IEEE Trans Multimed [J] 5 (1):52–70

    Article  Google Scholar 

  5. Berretti S, Bimbo A, Pala P (2010) 3D face recognition using iso-geodesic stripes. IEEE Trans Pattern Anal Mach Intell [J] 32(12):2162–2177

    Article  Google Scholar 

  6. Berretti S, Bimbo AD, Pala P (2012) Distinguishing facial features for ethnicity-based 3d face recognition. ACM Trans Intell Syst Technol [J] 3(3):45

    Google Scholar 

  7. Blanz V, Vetter T (1999) A morphable model for the synthesis of 3D faces. SIGGRAPH ’99 [C]:187–194. https://doi.org/10.1145/311535.311556

  8. Blanz V, Vetter T (2003) Face recognition based on fitting a 3D morphable model. IEEE Trans Pattern Anal Mach Intell (IEEE TPAMI) [J] 25(9):1063–1074

    Article  Google Scholar 

  9. Bronstein AM, Bronstein MM, Kimmel R (2005) Three dimensional face recognition. Int J Comput Vis [J] 64(1):5–30

    Article  Google Scholar 

  10. Bronstein AM, Bronstein MM, Kimmel R (2006) Expression-invariant representations of faces. IEEE Trans Image Process [J] 16(1):188–197

    Article  MathSciNet  MATH  Google Scholar 

  11. Cao C, Weng Y, Zhou S, Tong Y, Zhou K (2014) Facewarehouse: A 3D Facial Expression Database for Visual Computing. IEEE Trans Vis Comput Graph [J] 20(3):413–425

    Article  Google Scholar 

  12. Choi SE, Lee YJ, Lee SJ, Kang RP, Kim J (2011) Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn [J] 44(6):1262–1281

    Article  MATH  Google Scholar 

  13. Coates A, Ng AY (2012) Learning Feature Representations with K-means. Lect Notes Comput Sci [J]. 7700:561–580

    Article  Google Scholar 

  14. Desbrun M, Meyer M, Alliez P (2002) Intrinsic parameterizations of surface meshes. Eurographics [C] 21(3):209–218

    Google Scholar 

  15. Drira H, Amor B, Srivastava A, Daoudi M (2010) A riemannian analysis of 3D nose shapes for partial human biometrics. IEEE International Conference on Computer Vision [C] 30(2):2050–2057

    Google Scholar 

  16. Fakir M, Ahdid R, Taifi K et al (2015) Two-Dimensional Face recognition methods comparing with a riemannian analysis of Iso-Geodesic curves. J Electron Commer Organ [J] 13(3):15–35

    Article  Google Scholar 

  17. Feng LZ, Ou TX (2004) Bayesian face recognition using support vector machine and face clustering. IEEE Comput Soc Conf Comput Vis Pattern Recogn [C] 2:374–380

    Google Scholar 

  18. Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science [J] 315(5814):972–6

    Article  MathSciNet  MATH  Google Scholar 

  19. Ghahari A, Fatmehsari YR, Zoroofi RA (2009) A novel Clustering-Based feature extraction method for an automatic facial expression analysis system. International Conference on Intelligent Information Hiding and Multimedia Signal Processing [C]:1314–1317. https://doi.org/10.1109/IIH-MSP.2009.38

  20. Gilani SZ, Shafait F, Mian A (2015) Shape-based Automatic Detection of a Large Number of 3D Facial Landmarks. IEEE Conf Comput Vis Pattern Recognit (CVPR) [C] 07:4639–4648

    Google Scholar 

  21. Horng WB, Lee CP, Chen CW (2001) Classification of age groups based on facial features. Tamkang J Sci Eng [J] 4(4):183–192

    Google Scholar 

  22. Jahanbin S, Choi H, Liu Y et al (2008) Three dimensional face recognition using Iso-Geodesic and Iso-Depth curves. IEEE international conference on biometrics: Theory. Applications and Systems[C]:1–6. https://doi.org/10.1109/BTAS.2008.4699378

  23. Jahanbin S, Jahanbin R, Bovik AC et al (2013) Passive three dimensional face recognition using Iso-Geodesic contours and procrustes analysis. Int J Comput Vis [J] 105(1):87–108

    Article  Google Scholar 

  24. Jermyn IH, Kurtek S, Klassen SE, Srivastava A (2012) Elastic shape matching of parameterized surfaces using square root normal fields. European conference on computer vision [C]. pp 804–17

  25. Kurtek S, Drira H (2015) A comprehensive statistical framework for elastic shape analysis of 3D faces, vol 51

  26. Lopes AT, Aguiar ED, Souza AFD, Oliveira-Santos T (2017) Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order. Pattern Recogn [J] 61:610–628

    Article  Google Scholar 

  27. Luciano L, Hamza AB (2017) Deep learning with geodesic moments for 3D shape classification [J]. Pattern Recognition Letters

  28. Lyons MJ, Budynek J, Akamatsu S (1999) Automatic classification of single facial images. IEEE Trans Pattern Anal Mach Intell [J] 21(12):1357–1362

    Article  Google Scholar 

  29. Masoumi M, Hamza AB (2017) Spectral shape classification: A deep learning approach[J]. J Vis Commun Image Represent 43:198–211

    Article  Google Scholar 

  30. Mühling M, Korfhage N, Müller E et al (2017) Deep learning for content-based video retrieval in film and television production [J]. Multimed Tools Appl [J] 76(2):1–26

    Google Scholar 

  31. Otto C, Wang D, Jain A (2016) Clustering millions of faces by identity [J]. IEEE Trans Pattern Anal Mach Intell [J]. 40(2):1–1

    Google Scholar 

  32. Park HS, Jun CH (2009) A simple and fast algorithm for K-medoids clustering. Expert Syst Appl [J] 36(2):3336–3341

    Article  Google Scholar 

  33. Paysan P, Knothe R, Amberg B, Romdhani S, Vetter T (2009) A 3D face model for pose and illumination invariant face recognition. Advanced Video and Signal Based Surveillance [J]:296–301. https://doi.org/10.1109/AVSS.2009.58

  34. Pickup D, Sun X, Rosin PL et al (2016) Shape Retrieval of Non-rigid 3D Human Models[J]. Int J Comput Vis 120(2):169–193

    Article  MathSciNet  Google Scholar 

  35. Shan SL, Khalil-Hani M, Radzi SA, Bakhteri R (2016) Gender classification: a convolutional neural network approach. Turkish J Electr Eng Comput Sci [J] 24 (3):1248–1264

    Google Scholar 

  36. Srivastava A, Klassen E, Joshi SH, Jermyn IH (2010) Shape analysis of elastic curves in euclidean spaces. IEEE Trans Pattern Anal Mach Intell (IEEE TPAMI) [J] 33(7):1415–1428

    Article  Google Scholar 

  37. Surazhsky V, Surazhsky T, Kirsanov D et al (2005) Fast exact and approximate geodesics on meshes[J]. Acm Trans Graph 24(3):553–560

    Article  Google Scholar 

  38. Wu J, Smith WAP, Hancock ER (2007) Gender Classification using Shape from Shading. Proceedings of the British Machine Vision Conference [C]:50.1–50.10. https://doi.org/10.5244/C.21.50

  39. Xia J, He Y, Quynh D, Chen X, Hoi CH (2010) Modeling 3D Facial Expressions using Geometry Videos. Proceedings of ACM Multimedia (MM ’10) [C] 22 (1):591–600

    Google Scholar 

  40. Xia J, Quynh D, He Y, Chen X, Hoi SCH (2012) Modeling and compressing 3D facial expressions using geometry videos. IEEE Trans Circ Syst Video Technol [J] 22(1):77–90

    Article  Google Scholar 

  41. Yu D, Wu XJ (2017) 2DPCANEt: a deep leaning network for face recognition [J]. Multimed Tools Appl [J] 4:1–16

    Google Scholar 

  42. Zeng W, Hua J, Gu X (2009) Symmetric Conformal Mapping for Surface Matching and Registration. Int J CAD/CAM (IJCC) [J] 9(1):103–109

    Google Scholar 

  43. Zeng W, Samaras D, Gu X (2010) Ricci flow for 3D shape analysis. IEEE Trans Pattern Anal Mach Intell (IEEE TPAMI) [J] 32(4):662–677

    Article  Google Scholar 

  44. Zeng W, Gu X (2011) Conformal geometric methods in computer vision. IEEE CEWIT conference (CEWIT’11) [C]

Download references

Acknowledgements

This research was partially supported by the National Key Cooperation between the BRICS Program of China (No.2017YE0100500), National Key R&D Program of China (No. 2017YFB1002600, No.2017YFB1402105) and Beijing Natural Science Foundation of China (No.4172033). We thank the face database (Gavab and Texas3D) and method’s code provider in github. We also thank the provider of geodesic path tools (GeodesicLib; http://www.cs.technion.ac.il/vitus/papers/GeodesicLib.zip).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xingce Wang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, C., Wu, Z., Wang, X. et al. Constructing 3D facial hierarchical structure based on surface measurements. Multimed Tools Appl 78, 14753–14776 (2019). https://doi.org/10.1007/s11042-018-6839-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-018-6839-y

Keywords

Navigation